A proposed model using genetic algorithms for feature selection combined with classification algorithms achieved higher accuracy rates for premature ventricular contraction classification.
Does the use of genetic algorithms for feature selection improve the classification accuracy of premature ventricular contractions from ECG signals?
Applying genetic algorithms for feature selection improves the accuracy of machine learning models in classifying premature ventricular contractions from ECG signals.
Cardiac arrhythmia is one of the most important indicators of heart disease. Premature ventricular contractions (PVCs) are a common form of cardiac arrhythmia caused by ectopic heartbeats. The detection of PVCs by means of ECG (electrocardiogram) signals is important for the prediction of possible heart failure. This study focuses on the classification of PVC heartbeats from ECG signals and, in particular, on the performance evaluation of selected features using genetic algorithms (GA) to the classification of PVC arrhythmia. The objective of this study is to apply GA as a feature selection method to select the best feature subset from 200 time series features and to integrate these best features to recognize PVC forms. Neural networks, support vector machines and k-nearest neighbour classification algorithms were used. Findings were expressed in terms of accuracy, sensitivity, and specificity for the MIT-BIH Arrhythmia Database. The results showed that the proposed model achieved higher accuracy rates than those of other works on this topic.
Kaya et al. (Sun,) conducted a other in Premature ventricular contractions (PVCs). Genetic algorithms (GA) for feature selection and classification algorithms vs. Other works on this topic was evaluated on Classification accuracy, sensitivity, and specificity. A proposed model using genetic algorithms for feature selection combined with classification algorithms achieved higher accuracy rates for premature ventricular contraction classification.
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